2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
358
TD73
Legends A- Omni
Operations Management IV
Contributed Session
Chair: Jose M. Merigo, University of Chile, Av. Diagonal Paraguay 257,
Santiago, 8330015, Chile,
1 - Understanding And Managing Sequences Of Alignment Between
Technologies And Adopters: Case Research Of Implementations
Of A Health Screening Program
Jose Coelho Rodrigues, Researcher, INESC TEC and Faculty of
Engineering, University of Porto, Rua Dr Roberto Frias, Porto,
4200, Portugal, , Ana C Barros,
João Claro
Misalignments (lack of compatibility) between technologies and adopters cause
productivity losses in early stages of implementation projects. Alignment
management is particularly challenging when the adopter is a network of
organizations. We use multiple case research of implementations of a health
screening program in networks to understand how alignment efforts are
sequenced, focusing on the non-linear and cascading sequences. We provide
guidelines to improve implementations’ performance, by addressing why non-
linear and cascading sequences occur and what are their impacts on such projects.
2 - Mapping Production And Operations Management With
VOS Viewer
Jose M. Merigo, University of Chile, Av. Diagonal Paraguay 257,
Santiago, 8330015, Chile, Claudio Muller,
Sigifredo Laengle
The VOS viewer is a computer software that visualizes the bibliographic material
through different bibliometric indicators. This study develops a visualization of
production and operations management research by using the VOS viewer. The
analysis considers bibliographic coupling, co-citation, co-occurrence of keywords
and co-authorship for journals, documents, authors, institutions and countries.
The results indicate that this field is very diverse with two main cores focused on
engineering and management. Researchers from all over the World are making
important contributions in the field although the USA is still the leader.
TD74
Legends B- Omni
Optimization Methodology IV
Contributed Session
Chair: Xiang Gao, University of Minnesota, 111 Church Street SE,
Minneapolis, MN, 55455, United States,
1 - Auction Algorithms For Distributed Integer Programming
Problems With A Coupling Cardinality Constraint
Ezgi Karabulut, Georgia Institute of Technology,
755 Ferst Drive, NW, Atlanta, GA, 30308, United States,
, Shabbir Ahmed, George L Nemhauser
We are interested in optimizing discrete problems that use a common resource,
namely integer programming problems coupled with a cardinality constraint. Our
auction algorithm finds the optimal resource allocations when individual
problems are concave. When the problems are not concave, but rather have a
concave approximation; and we provide respective error bounds for the auction
algorithm.
2 - Fuzzification Of Search Techniques For Linear And
Nonlinear Optimization
Paul Eugene Coffman, Technical Leader, Virtual Manufacturing
and O.R., Ford Motor Company, 6100 Mercury Drive, Dearborn,
MI, 48126, United States, ,
Stephany Coffman-Wolph
Using a three-step framework any algorithm can be converted into an equivalent
abstract version known as a fuzzy algorithm. This goes beyond simply converting
the raw data into fuzzy data by converting both operators and concepts into their
abstract equivalents. Although precision may be reduced, it can be counteracted
by gains in computational efficiency. This presentation will discuss linear and
non-linear search algorithms that can benefit from fuzzification, results within the
context of potential applications, and the characteristics of an algorithm where
fuzzification can be utilized.
3 - Non-stationary Regret Analysis For A Non-convex Online
Learning Model
Xiang Gao, University of Minnesota, 111 Church Street SE,
Minneapolis, MN, 55455, United States, ,
Xiaobo Li, Shuzhong Zhang
In this talk we present a non-stationary regret analysis for an online learning
model with smooth but non-convex cost functions. The cost functions are
assumed to satisfy a condition which is more relaxed than the usual pseudo-
convexity. Moreover, the cost functions are assumed to satisfy an error bound
condition, which is implied by the analyticity. Under this framework, assuming
only the loss function values can be evaluated we design a learning algorithm
without the gradient information, and show that the regret of the algorithm is
proportional to the square root of the product of learning periods and the
variational budget which is the total variation of the optimal solutions measured
in distance.
TD75
Legends C- Omni
Behavioral Operations IV
Contributed Session
Chair: Junlin Chen, Associate Professor, Central University of Finance
and Economics, 39 South College Road, Haidian District, Beijing,
100081, China,
1 - Manufacturer Salespersons Relationships In Global Markets
Considering Inventory Policies And Cultural Effects
Sepideh Alavi, PhD Candidate, University of Wisconsin
Milwaukee, 1559, N Prospect Ave. Apt 309, Milwaukee, WI,
53202, United States,
The influence of salespersons’ intermediary behaviors on customer retention has
encouraged the manufacturers to develop and monitor strategies to increase
loyalty in salespersons (Keiko Yamakawa, 2002).Also, cultural types reflect
different trust characteristics in their relationship. Little is known about the
impacts of culture in manufacturer- salespersons’ relationships. This paper intends
to address this gap by investigating the research questions: What are the
inventory- related policy factors that enhance manufacturer-salespersons’
relationship? And does culture play a role in the manufacturer- salespersons’
relationship?
2 - Prediction Of SNS User’s Behavior Preference
Peng Zhu, Nanjing University of Science and Technology, School of
Economics and Management, 200 Xiaolingwei Street, Nanjing,
210094, China,
Analysis and prediction of user behavior has become significant means to
enhance the user experience in Social Networks Services(SNS). However, due to
features of social networks, the limitations of user’s time and energy, the social
relationships of most social users are incomplete and sparse, it restricts the
coverage and accuracy of user behavioral prediction. In response to these
problems, this paper extracts user potential social relationship, and by making use
of user preference information, it designs effective user preference consistency
algorithm. Meanwhile, it proposes a visualizer evaluation method, which also can
evaluate the performance of prediction algorithm from micro level.
3 - Strategic Consumer Behavior In Single Rider Lanes At
Adventure Parks
Arpit Goel, PhD Student, Stanford, 475 Via Ortega, Huang
Engineering Center, Stanford, CA, 94305, United States,
Adventure park rides often have separate lanes for single riders. Single riders are
added to any ride tram which is not fully occupied, which increases the efficiency
of the queuing process. Thus single rider lanes are usually served much faster
than regular lanes. But often these lanes are strategically used by families to
expedite their waiting times, the risk being the family not being able to take the
same ride tram. We model this scenario as a stochastic process, understanding the
strategic tradeoffs, showing situations where this strategic behavior significantly
harms the welfare, and thereby implying some managerial ideas for adventure
parks to further improve their queuing process.
4 - Crowding-out And Overjustification Effects On Pro-social
Behaviors: A Quasi-experimental Study
Dandan Qiao, Tsinghua University, HaiDian District, Beijing,
China, Shun-Yang Lee,
Andrew B Whinston, Qiang Wei
We explore how external incentives would influence one’s pro-social behavior
both in the short term and in the long run. Using a large data set on Amazon
product reviews (1997-2014), we design a quasi-experimental approach by
combining a propensity score matching (PSM) and a difference-in-differences
(DiD) method. Several novel measures are proposed to capture reviewers’ writing
style and quality by applying linguistic, language processing, and machine
learning techniques. Through estimating a series of fixed-effect DiD models, we
find evidence consistent with reciprocity, crowding-out, and overjustification
effects.
TD73